Survival Prediction in Allogeneic Haematopoietic Stem Cell Transplant Recipients Using Pre- and Post-Transplant Factors and Computational Intelligence

Abstract

Advancements in artificial intelligence (AI) predictive models have emerged as valuable tools for predicting survival outcomes in allogeneic haematopoietic stem cell transplantation (allo-HSCT). These models primarily focus on pre-transplant factors, while algorithms incorporating changes in patient's status post-allo-HSCT are lacking. The aim of this study was to develop a predictive soft computing model assessing survival outcomes in allo-HSCT recipients. In this study, we assembled a comprehensive database comprising of 564 consecutive adult patients who underwent allo-HSCT between 2015 and 2024. Our algorithm selectively considers critical parameters from the database, ranking and evaluating them based on their impact on patient outcomes. By utilising the Data Ensemble Refinement Greedy Algorithm, we developed an AI model with 93.26% accuracy in predicting survivorship status in allo-HSCT recipients. Our model used only seven parameters, including age, disease, disease phase, creatinine levels at day 2 post-allo-HSCT, platelet engraftment, acute graft-versus-host disease (GvHD) and chronic GvHD. External validation of our AI model is considered essential. Machine learning algorithms have the potential to improve the prediction of long-term survival outcomes for patients undergoing allo-HSCT.